Outage Mitigation Research Articles

Hybrid power plants: An effective way of decreasing loss-of-load expectation

Diversifying variable renewable resources by combining wind, solar photovoltaic, and battery assets in a hybrid power plant can increase renewable energy usage efficiency and improve system flexibility, particularly in distributed energy systems. However, the resilience impact of these systems, particularly outage mitigation, can be difficult to quantify due to uncertainty in resource, energy demand, and outage occurrence. This study outlines a framework to quantify the incremental benefit of hybrid power plant assets for reducing loss-of-load expectation during random outage events. Hybrid power plant performance during outages (considering varying duration and severity) is simulated using a Monte Carlo methodology to reflect uncertainty associated with renewable resource, load demand, and outage timing. Results demonstrate the additional incremental value from increasingly hybrid designs, in which relative capacities of wind, solar photovoltaic, and storage assets contribute to lower loss-of-load expectation than the constituent technologies would alone. The value of added wind or solar capacity increases as the plant composition approaches an equal split. The value of added battery capacity depends on the outage duration and severity, but the first 50 MWh of added storage capacity is the most valuable for reducing the loss-of-load expectation for all plant designs.

Real-time outage management in active distribution networks using reinforcement learning over graphs

Self-healing smart grids are characterized by fast-acting, intelligent control mechanisms that minimize power disruptions during outages. The corrective actions adopted during outages in power distribution networks include reconfiguration through switching control and emergency load shedding. The conventional decision-making models for outage mitigation are, however, not suitable for smart grids due to their slow response and computational inefficiency. Here, we present a graph reinforcement learning model for outage management in the distribution network to enhance its resilience. The distinctive characteristic of our approach is that it explicitly accounts for the underlying network topology and its variations with switching control, while also capturing the complex interdependencies between state variables (along nodes and edges) by modeling the task as a graph learning problem. Our model learns the optimal control policy for power restoration using a Capsule-based graph neural network. We validate our model on three test networks, namely the 13, 34, and 123-bus modified IEEE networks where it is shown to achieve near-optimal, real-time performance. The resilience improvement of our model in terms of loss of energy is 607.45 kWs and 596.52 kWs for 13 and 34 buses, respectively. Our model also demonstrates generalizability across a broad range of outage scenarios.

Grid-Aware Tradeoff Analysis for Outage Mitigation Microgrids at Emerging Resilience Hubs

Grid-Aware Tradeoff Analysis for Outage Mitigation Microgrids at Emerging Resilience Hubs

Value Stacking of a Customer-Sited BESS for Distribution Grid Support: A Utility Case

This paper proposes a value stacking strategy for a utility-owned, customer-sited battery energy storage system for distribution grid support. The proposed strategy includes three steps: application identification, performance evaluation, and battery system planning. Outage mitigation, non-wires-alternative solution, and voltage support are identified as the primary, secondary, and tertiary applications for the proposed storage system. Reliability performance and economic benefit are calculated and compared for each application in five locations in the Commonwealth of Virginia. Planning results including placement, sizing and scheduling of the battery system are presented with real utility experience and constraints. This work contributes to providing a general and ready-to-use planning methodology for distribution system planners to deploy battery storage systems within service territories, strengthen system reliability performance, and achieve sustainability goals.

A protocol for selecting viable transmission line arrester for optimal lightning protection

Direct lightning strikes on overhead lines and towers causes power system outages. Optimal surge arrester placement is a practical and economical protection technique. From research trends, three types of transmission line arresters are in practice: Non-Gapped Line Arrester (NGLA), Externally Gapped Line Arrester (EGLA) and Multi Chamber Insulator Arrester (MCIA). This study proposed a simplified guide for selecting the most suitable line arrester considering the placement configurations, double circuit outage prevention and energy capabilities. Electromagnetic Transient Program was implemented to analyze back flashover performance of a typical 275 kV double circuit transmission line in Malaysia. Critical flashover parameters considered are lightning current magnitude, tower footing resistance, and power frequency angle. Analyzed results from the three arresters found that installing four arresters, two per circuit, at the lower and upper phases respectively, is the most optimal and universal double outage mitigation technique. Energy comparison reveals that MCIA discharges 54% and 44% less energy than NGLA and EGLA respectively. Also, from 132 kV system results, MCIA discharges 74% and 22% lesser energy respectively. From further validation of the simulation model, NGLA discharged 23.8% more energy than EGLA from experimental results. The proposed guide is applicable to double circuit lines below 275 kV.

Mobile Energy Storage Systems: A Grid-Edge Technology to Enhance Reliability and Resilience

Increase in the number and frequency of widespread outages in recent years has been directly linked to drastic climate change necessitating better preparedness for outage mitigation. Severe weather conditions are experienced more frequently and on larger scales, challenging system operation and recovery time after an outage. The impact is more evident and concerning than before, considering the increased dependency on electricity in all aspects of our lives.

A novel low-cost solution for mitigating the loss of power supply probability in grid-tied Solar PV systems during daytime grid-outage scenario

In this paper, a novel method has been proposed and implemented to mitigate the loss of power from grid-connected solar PV systems during the grid outage condition. This loss during daytime grid outages usually occurs because of the built-in islanding feature of the commercially available grid-tied inverters. Unlike other existing grid outage mitigation methods such as; using the expensive hybrid inverters and other battery backup inverters, the proposed method introduces a battery-less topology which can avoid the significant cost of battery pack itself and associated auxiliary control circuits, its operation and maintenance. An optimized low capacity (around 5% of grid-tied inverter rating) DC-AC interface system in the form of a stand-alone inverter has been introduced to provide the reference AC bus for keeping the grid-tied solar inverter operational even during the daytime grid outage. The interface system is energized from a solar PV-powered DC bus instead of the commonly used high-capacity battery backup. The method also ensures the maximum available energy extraction from a grid-connected solar PV Plant without renovating the existing grid-tied solar inverters. The experimental validation has been done by a practical 2kW p grid-tied inverter and a 200VA stand-alone inverter interfaced solar PV system operation and control under sunny and cloudy day scenario. The proposed low-cost grid outage mitigation method is a generalized one and hence can be very useful for scalable grid-connected solar PV power plants also.

Mitigating Cascading Outages in Severe Weather Using Simulation-Based Optimization

Severe weather events can trigger cascading power outages and lead to significant losses. In this work, we investigate cascading outage mitigation under severe weather conditions. Given day-ahead weather forecasts and component failure models, we aim to identify a set of power lines that can be hardened to minimize the expected impact of potential cascading outages. Since the expected load shedding cannot be expressed as an explicit function of line hardening decisions and system states, we developed a cascading outage simulator to estimate the expected value of load shedding under various initial weather-related disruption scenarios generated using a weather forecast. To avoid massive enumeration of all possible combinations of line hardening decisions and reduce the simulation efforts, we employed an efficient simulation-based optimization approach that quickly identifies the (near) optimal line hardening decisions in the presence of both large simulation noises due to the highly variable initial disturbances and system states, and significant randomness in the subsequent cascades. The algorithm is also able to utilize parallel computing to dramatically reduce computation time to support decision making in preparation for severe weather conditions. We performed a case study on the Northeast Power Coordinating Council (NPCC) 140-bus system model to demonstrate that our approach can significantly improve power grid resilience to adverse weather events.

Stacked Revenue and Technical Benefits of a Grid-Connected Energy Storage System

This paper proposes a comprehensive evaluation of stacked revenue generated from grid-connected energy storage systems (ESSs). The stacked revenue from an ESS cannot be calculated by merely aggregating the benefits from various applications (e.g., energy arbitrage, frequency regulation, and outage mitigation) as the ESS may not be available for all types of applications during the same time intervals. This is because a quantity committed to one market may not be committed to another. In this paper, different types of applications for grid-connected ESSs are identified, and a model incorporating component reliability, power system operation constraints, and storage system operation constraints is developed to evaluate the composite revenue generated from these applications. In this model, the types of applications of ESSs are prioritized according to their intended contributions and system operating conditions. Sequential Monte Carlo simulation is used for evaluating the reliability improvement and a quadratically constrained linear programing model is built for estimating the maximum revenue from arbitrage and regulation markets. The proposed method is demonstrated on the IEEE reliability test system using historical PJM price data.

Profitable Multi-Use of Battery Energy Storage in Outage Mitigation and as Frequency Reserve

Battery energy storage (BES) systems have high capital costs and low operational costs. This means that in order to introduce profitable BES applications, a high utilization rate should be achieved. The target of this study was to analyze the profitability of the BES system connected to a low voltage distribution network. The BES system is used for the power system’s frequency containment reserve (FCR-D) and to minimize customer interruption costs (CIC) according to the Finnish regulation model. Investment costs, including the required power electronic converters as well as the costs related to the lifetime decrease of the BES system during the 15-year period, are taken into account in the economic analysis. The FCR-D and CIC mitigation incomes are based on real measured data from the Finnish TSO and DSO. According to the results, the BES is a profitable investment if both FCR-D cost and CIC mitigation are used.

A Predictive Energy Management System Using Pre-Emptive Load Shedding for Islanded Photovoltaic Microgrids

This paper presents an energy management system (EMS) for an islanded microgrid with photovoltaic generation and battery storage. The system uses a predictive approach to set operational schedules in order to minimize system-wide outages in the microgrid, specifically through pre-emptive load shedding. Four-times daily updated online weather forecasts are combined with the photovoltaic system model to predict energy production over a 48 h period. These predictions are used, along with load forecasts and a model of the energy storage system, to predict the state-of-charge and characterize potential upcoming outages. Outage mitigation actions using pre-emptive load shedding are then planned and executed to avoid outages or minimize the duration of unavoidable outages. The approach also features bounds on the battery state-of-charge to account for uncertainties in the estimate of the stored energy. The EMS has been implemented using an event-driven framework with TCP/IP communication, which is modular and extensible to more complex system configurations. The approach has been validated through simulations and experiments, which demonstrate its feasibility and potential, for the chosen test scenario, to reduce the outage duration by 87% to 100%.

Integration of Hybrid Wind-Solar Power Conversion System and Dynamic Voltage Restorer for Voltage Regulation and Energy Conservation

This paper presents the concept of optimal utilisation of hybrid wind-solar system as dynamic voltage restorer (DVR) for voltage sag, voltage swell, outage and unbalanced voltage mitigation. In addition to the voltage regulation, the proposed DVR reduces the energy consumption from the utility grid during both daytime and night time. However, the reduction of energy consumption is always desirable for the reduction of panel tariff, electricity demand and global warming gasses. The MATLAB Simulink and experimental results are presented to validate the advantages of the proposed system.

Integration of hybrid wind-solar power conversion system and dynamic voltage restorer for voltage regulation and energy conservation

This paper presents the concept of optimal utilisation of hybrid wind-solar system as dynamic voltage restorer (DVR) for voltage sag, voltage swell, outage and unbalanced voltage mitigation. In addition to the voltage regulation, the proposed DVR reduces the energy consumption from the utility grid during both daytime and night time. However, the reduction of energy consumption is always desirable for the reduction of panel tariff, electricity demand and global warming gasses. The MATLAB Simulink and experimental results are presented to validate the advantages of the proposed system.

Optimal utilisation of photovoltaic system as dynamic voltage restorer for voltage regulation and energy conservation

This paper presents a novel optimal utilisation of photovoltaic (PV) system as dynamic voltage restorer (PV-DVR) for voltage sag, voltage swell, outage and unbalanced voltage mitigation during both daytime and night time. This novel PV system operated DVR reduces the energy consumption from the utility grid by disconnecting the utility grid from the load through semiconductor switches, when the PV system generates excessive or equal real power to the required load demand. However, the reduction of energy consumption is always desirable for the reduction of panel tariff and global warming gasses. In this paper, PV module is connected to the DC side of DVR through the low step-up DC-DC converter with fuzzy-based perturb and observe maximum power point tracking algorithm to eliminate the drawback of the conventional PV system by tracking maximum power point of the PV array is presented. The proposed PV-DVR, if connected at the terminals of a small industry or a home or a small educational institution, can avoid interruption of power, use of individual stabiliser and potential panel tariff over a 12-hour period. A MATLAB Simulink model with a 10 kW PV module is presented to validate the advantage of the proposed system.

Optimal Utilisation of Photovoltaic System as Dynamic Voltage Restorer for Voltage Regulation and Energy Conservation

This paper presents a novel optimal utilisation of photovoltaic (PV) system as dynamic voltage restorer (PV-DVR) for voltage sag, voltage swell, outage and unbalanced voltage mitigation during both daytime and night time. This novel PV system operated DVR reduces the energy consumption from the utility grid by disconnecting the utility grid from the load through semiconductor switches, when the PV system generates excessive or equal real power to the required load demand. However, the reduction of energy consumption is always desirable for the reduction of panel tariff and global warming gasses. In this paper, PV module is connected to the DC side of DVR through the low step-up DC-DC converter with fuzzy-based perturb and observe maximum power point tracking algorithm to eliminate the drawback of the conventional PV system by tracking maximum power point of the PV array is presented. The proposed PV-DVR, if connected at the terminals of a small industry or a home or a small educational institution, can avoid interruption of power, use of individual stabiliser and potential panel tariff over a 12-hour period. A MATLAB Simulink model with a 10 kW PV module is presented to validate the advantage of the proposed system.

Power Outage, Business Continuity and Businesses' Choices of Power Outage Mitigation Measures

Problem statement: Power outage has been mentioned as one of the most experienced and perceived risks by various types of businesses and organizations. Thus, reducing the impacts of power outage has become a key agenda in business continuity planning. Back-up or stand-by generators are among the most well known measures taken by power consumers to tackle the power outage problem. Approach: A survey was conducted to understand various aspects of power outage and the impacts of power outrage on businesses. In addition a choice experiment method was used to derive businesses’ preferences for different power outage mitigation measures that would have a range of prices, space requirements, air and noise pollution as well as mobility attributes using a sample of Canadian businesses located in the . Sample was drawn from businesses operating in the Greater Toronto Area (GTA) and surrounding communities in Ontario, Canada. Results: It was found that majority of businesses have experienced and unconvinced by power outage and consider it to be a major risk to their operations. Production loss, data loss, damage to equipment and loss of lighting are the most significant sources for inconveniences caused by the power outages. It was found that a considerable number of businesses have not taken appropriate measures to mitigate this risk. Estimations from the responses revealed that sample businesses had a positive willingness to pay for power outage reduction and that businesses prefer power outage mitigation measures that are least costly, have low levels of air and noise pollution and occupy smaller spaces respectively. Conclusion: An uninterrupted power supply is an important element of business continuity in today’s business world. Although many businesses are not fully prepared against power outages, they are willing to pay for low cost and low pollution power outage mitigation measures.

Cross-Layer Admission Control Policy for CDMA Beamforming Systems

A novel admission control (AC) policy is proposed for the uplink of a cellular CDMA beamforming system. An approximated power control feasibility condition (PCFC), required by a cross-layer AC policy, is derived. This approximation, however, increases outage probability in the physical layer. A truncated automatic retransmission request (ARQ) scheme is then employed to mitigate the outage problem. In this paper, we investigate the joint design of an AC policy and an ARQ-based outage mitigation algorithm in a cross-layer context. This paper provides a framework for joint AC design among physical, data-link, and network layers. This enables multiple quality-of-service (QoS) requirements to be more flexibly used to optimize system performance. Numerical examples show that by appropriately choosing ARQ parameters, the proposed AC policy can achieve a significant performance gain in terms of reduced outage probability and increased system throughput, while simultaneously guaranteeing all the QoS requirements.